arXiv subjects

James P. Larentzos

Publications and source records attributed to James P. Larentzos.

4 recordsLinked to original sources

arXiv2026

Statistical Mechanics of Density- and Temperature-Dependent Potentials: Application to Condensed Phases within GenDPDE

Coarse-grain Lagrangian methods, such as Dissipative Particle Dynamics ( Hoogerbrugge et al., EPL, 1992), are suitable for describing mesoscopic fluid systems that include thermal fluctuations. However, the realistic simulation of liquids using these methods represents a longstanding problem. In this work, we develop a local thermodynamic (LTh) model for the description of condensed phases within the framework of the Generalized Dissipative Particle Dynamics with Energy Conservation (GenDPDE) method (Bonet Avalos et al., PCCP 2019). Such a model is appropriate for the analysis of liquids, due to the explicit account of the thermal expansion coefficient and isothermal compressibility at the mesoscale. We demonstrate the accuracy of the LTh model by examining the thermodynamic properties of argon at both liquid and supercritical conditions, through equilibrium simulations performed around two key reference states (125.7 K, 85.31 MPa, 1419.7 kg/m3 for liquid Ar, and 418.8 K, 85.31 MPa, 695.99 kg/m3 for supercritical Ar). Remarkably, we show that the model is also valid over a range of thermodynamic conditions near the reference states, allowing a correct description of the physics of systems with spatial variations in density and temperature. We further derive analytical expressions for the macroscopic pressure and energy equations of state based on the model parameters, discussing their validity and limitations. We demonstrate that, even at the mean-field level, accurately capturing local particle arrangements is essential for predicting macroscopic thermodynamic properties from mesoscopic data. We also assess the applicability of the HNC approximation in predicting the radial distribution function of the GenDPDE system, exploring its strengths and limitations. With the LTh model, GenDPDE offers a dependable and versatile tool for analysing condensed phases through coarse-grain techniques.

cond-mat.soft↗

arXiv2026

Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net

Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging has been deriving relationships for properties that depend on subtle microstructural features and not average properties. One such example is the initiation of energetic materials under dynamical loading, dominated by energy localization on microstructural features such as pores, cracks, and interfaces. We propose a conditional convolutional neural network to predict the shock-induced temperature field as a function of shock strength, for a wide range of microstructures, and obtained via two different simulation methods. The proposed model, denoted MISTnet2, significantly extends prior work that was limited to a single shock strength, model, and type of microstructure. MISTnet2 can contribute to bridging atomistics with coarse-grain simulations and enable first principles predictions of detonation initiation and safety of this class of materials.

cond-mat.mtrl-sci↗

arXiv2024

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

cond-mat.mes-hall↗

arXiv2019

Energy localization and excess fluctuations from long-range interactions in equilibrium molecular dynamics

Molecular Dynamics (MD) simulations of standard systems of interacting particles ("atoms") give excellent agreement with the equipartition theorem for the average energy, but we find that these simulations exhibit finite-size effects in the dynamics that cause local fluctuations in energy to deviate significantly from the analogous energy fluctuation relation (EFR). The main conclusion of our study is that systems separated into nanometer-sized "blocks" inside much larger simulations exhibit excess fluctuations in potential energy (pe) that diverge inversely proportional to T in a manner that is strongly dependent on the range of interaction. Specifically, at low T with long-range interactions pe fluctuations exceed the EFR by at least an order of magnitude, dropping abruptly to below the EFR when interactions include only 1st-neighbor atoms. A simplistic model that includes 2nd-neighbor interactions matches the behavior of the excess pe fluctuations, but only if the 2nd-neighbor terms are not included in Boltzmann's factor, attributable to energy localization due to anharmonic effects. Characterizing energy correlations as a function of time and distance reveals that excess pe fluctuations in a block coincide with negative pe correlations between neighboring blocks, whereas reduced pe fluctuations coincide with positive pe correlations. Indeed, anomalous pe fluctuations in small systems at low T can be quantified by using the net energy in Boltzmann's factor that includes the pe from a surrounding shell of similarly small systems, or equivalently an effective local temperature. Our analysis elucidates the source of non-Boltzmann fluctuations, and the need to include mesoscopic thermal effects from the local environment for a consistent theoretical description of the equilibrium fluctuations in MD simulations of standard models with long-range interactions.

physics.comp-ph↗